提出单次前向传播的3D人体姿态上采样方法，兼顾精度与速度。
Efficient 2D to Full 3D Human Pose Uplifting including Joint Rotations
- 直接端到端预测3D姿态和关节旋转，避免耗时的逆运动学计算。
- 旋转估计精度达当前最优，关节定位比HMR模型更准，速度提升150倍。
- 适用于体育分析等需高精度3D人体动作建模的场景。
在体育分析中，准确捕捉身体关节的3D位置和旋转对理解运动员生物力学至关重要。尽管人体网格恢复（HMR）模型能估计关节旋转，但其关节定位精度通常低于3D人体姿态估计（HPE）模型。近期工作通过结合3D HPE模型与逆运动学（IK）来同时估计位置和旋转，但该方法计算开销大。为此，我们提出一种新型2D到3D姿态上采样模型，可在单次前向传播中直接估计包含关节旋转的3D人体姿态。我们研究了多种旋转表示、损失函数及训练策略，包括有无真实旋转标签的情况。实验表明，所提模型在旋转估计上达到当前最优性能，速度比基于IK的方法快150倍，且在关节定位精度上优于HMR模型。
原文摘要 · Abstract (English)
In sports analytics, accurately capturing both the 3D locations and rotations of body joints is essential for understanding an athlete's biomechanics. While Human Mesh Recovery (HMR) models can estimate joint rotations, they often exhibit lower accuracy in joint localization compared to 3D Human Pose Estimation (HPE) models. Recent work addressed this limitation by combining a 3D HPE model with inverse kinematics (IK) to estimate both joint locations and rotations. However, IK is computationally expensive. To overcome this, we propose a novel 2D-to-3D uplifting model that directly estimates 3D human poses, including joint rotations, in a single forward pass. We investigate multiple rotation representations, loss functions, and training strategies - both with and without access to ground truth rotations. Our models achieve state-of-the-art accuracy in rotation estimation, are 150 times faster than the IK-based approach, and surpass HMR models in joint localization precision.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。